The network have to learn to produce images having the same structure as ground truth images. This goal can be achieved by using loss function that preserves the structure as is the case with SSIM loss. SSIM score [1]} is used to measure the similarity of structure between the two images, rather than a pixel-to-pixel difference like the PSNR does for example. It is computed between two windows \(x_j\)  and \(y_j\)  in the image. It is given by (REF ) which combines three comparative measures: luminance (REF ), contrast (REF ) and structure (REF ).
\(SSIM(x_j,y_j)=[l(x_j,y_j)]^\alpha \cdot [c(x_j,y_j)]^\beta \cdot [s(x_j,y_j)]^\gamma , \) 
